julian
20a3cb3e80
Fills /analyze vehicle.body_type + confidence (car / motorcycle / bus / truck from COCO, mapped to the shared vocabulary) for the Car Wash desk's category suggestion (venue-modules.md §Vehicle category from vision). Advisory: the operator decides, a confident downgrade is flagged, nothing is gated on it. - vision_service/vehicle.py: pure numpy/cv2 letterbox (pad 114, raw BGR), stride-grid decode, class-agnostic NMS, one vehicle per frame (the box holding the plate's centre, else the largest); YoloxVehicleDetector on onnxruntime CPU, 2 intra-op threads. - recognizer.py: WithVehicle composes the stage over any plate recognizer (stub included); a failing stage yields vehicle=null + a "vehicle: …" note in /health.detail — never costs the plate read. model_version reads "<plate>+yolox:yolox_s.onnx@640". - settings: VISION_VEHICLE_MODEL_PATH (unset = off), _INPUT_SIZE (640), _MIN_CONFIDENCE (0.4, the detector's floor; the flag threshold is site config). - Dockerfile bakes yolox_s.onnx (best-effort curl at build; no network → stage off) and sets the path; compose forwards it (empty = off); .env.example documents it. - Measured on four real dev entry frames (DS-2CD1047G3H, 2560×1440): car at 0.83–0.88 in ~240–330 ms; empty lane with a person → none. - tests/test_vehicle.py: decode/NMS/pick/letterbox on synthetic tensors, the composition, and a missing-model /health. Wiki: opencv-anpr-service, venue-modules, log. Claude-Session: https://claude.ai/code/session_01FWncR69HgGPuei1dLrW3cU
48 lines
2.0 KiB
Python
48 lines
2.0 KiB
Python
"""Runtime configuration, from environment (prefix VISION_).
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Offline-first: every default is local and works with no network. The recognizer is
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chosen by `recognizer` — "stub" (no models, deterministic placeholder) or "fast_alpr"
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(the real MIT YOLOv9+CCT/ONNX stack, installed via the `alpr` extra).
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"""
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from __future__ import annotations
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from typing import Literal
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from pydantic_settings import BaseSettings, SettingsConfigDict
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class Settings(BaseSettings):
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model_config = SettingsConfigDict(env_prefix="VISION_", env_file=".env", extra="ignore")
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host: str = "0.0.0.0"
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port: int = 8089
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# Which recognizer to load. "stub" needs no model weights (boots anywhere, for
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# dev/CI); "fast_alpr" loads the real models (requires the `alpr` extra installed).
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recognizer: Literal["stub", "fast_alpr"] = "stub"
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# fast-alpr model names (only used when recognizer="fast_alpr"). Defaults match the
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# library defaults; swap the OCR for the 40+country EU model to benchmark Albanian
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# plates. See wiki/entities/opencv-anpr-service.md "Recognizer evaluation".
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detector_model: str = "yolo-v9-t-384-license-plate-end2end"
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ocr_model: str = "cct-xs-v2-global-model"
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# Below this OCR confidence the read is returned but flagged low_confidence, so the
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# Node side can fall back to the ticket path rather than trust it.
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min_confidence: float = 0.5
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# Vehicle stage (phase A — venue-modules.md §Vehicle category from vision): a YOLOX
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# ONNX graph (Apache-2.0) run beside the plate recognizer. Unset = stage off (the
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# response's `vehicle` stays null). Bake the file into the image (models/), never a
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# path an operator can write (vision-service-hardening.md).
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vehicle_model_path: str | None = None
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vehicle_input_size: int = 640
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# Detection score floor for a vehicle box to count at all (the Node side applies the
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# site's own, stricter threshold before it FLAGS anything).
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vehicle_min_confidence: float = 0.4
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def get_settings() -> Settings:
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return Settings()
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